Comment on âIn Silico Modeling of Gastrointestinal Drug Absorption

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Commentary on "In silico modeling of gastrointestinal drug absorption: Predictive performance of three physiologically based absorption models" David B Turner, Bo Liu, Nikunjkumar Patel, Shriram M. Pathak, Sebastian Polak, Masoud Jamei, Jennifer B. Dressman, and Amin Rostami-Hodjegan Mol. Pharmaceutics, Just Accepted Manuscript • DOI: 10.1021/acs.molpharmaceut.6b00469 • Publication Date (Web): 08 Jul 2016 Downloaded from http://pubs.acs.org on July 9, 2016

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Molecular Pharmaceutics

Commentary on "In silico modeling of gastrointestinal drug absorption: predictive performance of three physiologically based absorption models" David B. Turner1*, Bo Liu1, Nikunjkumar Patel1, Shriram M. Pathak1, Sebastian Polak1,2, Masoud Jamei1, Jennifer Dressman4, Amin Rostami-Hodjegan1,3 1. Simcyp Limited (A Certara Company), Blades Enterprise Centre, John Street, Sheffield, S2 4SU, UK. 2. Faculty of Pharmacy, Jagiellonian University Medical College, Krakow, Poland. 3. Manchester Pharmacy School, The University of Manchester, Manchester, UK, M13 9PT. 4. Faculty of Biochemistry, Chemistry and Pharmacy, Goethe University, Frankfurt am Main, Germany *Corresponding Author: David B Turner, Simcyp Limited, Blades Enterprise Centre, John Street, Sheffield, South Yorkshire, S2 4SU, Tel: 0114 292 2327 Fax: 0114 292 2333 Email: [email protected]

ACS Paragon Plus Environment


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To the Editor, Sjogren et al. (2016)

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recently published a report which for the first time compares three different

modelling and simulation (M&S) tools for the prediction of the rate and extent of oral drug absorption. The authors focus on two commercially available Physiologically-Based PharmacoKinetic (PBPK) tools widely used in industry and academia (‘Simcyp’ and ‘GastroPlusTM’) and an AstraZeneca internal tool (‘GI-Sim’) which is not publically available. Such an evaluation is of great interest both to the tool providers and the wider scientific audience. The authors arrived at a very strong conclusion viz. “… the results indicate that GI-Sim and GastroPlusTM performs (sic) better than Simcyp in predicting the intestinal absorption of the incompletely absorbed drugs when a higher degree of accuracy is needed”. It is our contention that the study undertaken does not merit such a strong conclusion to be drawn for a variety of reasons. These include the use of default drug-specific parameters, issues with the convolution procedure used to estimate pharmacokinetic (PK) parameters from fraction absorbed data, and the difficulty in separating the influence of modeller bias in selection of input parameter values and in selection of model options from the performance of the software platform itself. Herein we provide a high-level criticism of the published work, underscored by illustrative examples. Additionally we intend to publish detailed analyses on a case-by-case basis at a later date. AstraZeneca (GI-Sim), SimulationsPlus (GastroPlusTM) and Certara (Simcyp) are all participants in the Oral Bioavailability Tools (OrBiTo) 2 consortium funded under the European Innovative Medicine Initiative (IMI) which involves a total of 26 partners from industry, academia, and SMEs. Comparison of the performance of the three software tools is a key task of OrBiTo and has been undertaken using a database created within OrBiTo which contains information for 43 different compounds from a total of 165 human studies and over 600 clinical study arms as well as pertinent physico-chemical and dosing information. As a part of this multicentre exercise, over 4,000 simulations were undertaken by 15 participating institutions; we will not comment further on this on-going work as it will be published in due course. However, we would like to signal that a great deal of effort was required for the OrBiTo investigators to identify, delineate and find solutions for the typical pitfalls that can arise


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when attempting to compare modelling tools – this, at least in part, explains why there have not been any head-to-head comparisons published in the open literature to date. The three modelling tools, at first examination, have analogous model features, data inputs etc. However, they also differ in many ways, including how inputs are handled, and which default options for certain model features and/or their parameters exist. Hence, distinguishing between what the ‘tool’ is doing vs. what the ‘modeller’ decides is difficult, especially when the tools take approaches that are not equivalent in all aspects. Despite the stated intentions of the authors, it appears that they were unable to avoid a number of these pitfalls when building predictive simulations. Below we outline some of these pitfalls for the benefit of readers who may be contemplating similar comparisons. 1. Is it possible to compare simulation outcomes where the tools do not provide the analogous outputs? The GI-Sim program is not capable of predicting plasma drug concentration (Cp) profiles and the associated metrics (AUC, Cmax and Tmax) obtained from clinical studies on an a priori basis, whereas both Simcyp and GastroPlusTM can do this as part of a “bottom-up” approach. In an attempt to “level the playing field”, Sjögren et al.

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constructed Cp profiles from fraction absorbed for all three

programs using a numerical convolution approach; bi- or tri-exponential models were parameterised mostly from IV clinical studies using an independent 4th platform, WinNonLin (Certara). In doing so, the authors assumed that the disposition models so reconstructed would lead to the same output as those from the original software - their comparisons refer to recovery of these PK parameters as if they had been obtained from that specific software. For orally administrated drugs all PBPK disposition models, regardless of whether they have a minimal number of compartments or are more complete models with separate compartments for all major tissues, have an initial dilution step in the liver prior to the drug entering the systemic circulation. This process in itself can have a profound impact on predicted Cmax meaning that it is, in general, incorrect to refer to a Cmax derived from the described convolution process as if it has been predicted by a PBPK program. Hence the values compared were NOT direct outputs from the platforms, but rather generated by the convolution



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process that has to be used for GI-Sim - simply because GI-Sim is not capable of producing outputs directly comparable to observed values without additional manipulation of observed clinical data via deconvolution. Furthermore, the reader should note that the majority of the IV studies used for the deconvolution step were with different subjects to the oral studies; i.e., they were not obtained from crossover studies. The quality of the data and parameterisation of the unit impulse response (UIR) are equally important when the metrics to assess model performance are AUC and Cmax. Hence ambiguities in the convolution assessment approach and parameterisation make it difficult for a reader to ascertain whether errors in the absorption prediction are compensated by errors in the opposite direction in estimated disposition parameters. As one example, the authors of this Letter observed a considerable impact (a two-fold increase) in Cmax convoluted from a Simcyp absorption profile for oral aprepitant when the bi-exponential model parameters were re-estimated from the same IV data used by Sjogren et al. (the convoluted AUC was not significantly changed). For three of the 12 compounds IV studies were not available so the convolution parameters were derived from oral studies, presumably based on the absorption (fa) profile predicted by GI-Sim. Again, this may have introduced bias into the comparison. Moreover, in a previous publication, 3 Sjogren et al. rightly highlighted the complexities related with transporters, entero-hepatic recirculation (EHR), gut-wall metabolism, auto-induction and autoinhibition of metabolism and dose-nonlinearity in clearance for many of the studied compounds, but have unfortunately completely neglected them in this study. Rather, they used a numerical convolution approach which did not, and cannot, account for impact of such processes on overall AUC and Cmax prediction, thus confounding the outcomes of the analyses and the conclusions. For example, the AUCs for the six fexofenadine (FEXO) doses/formulations studied were underpredicted by all three software tools to varying degrees. There is more than one possible explanation for this general under-prediction. It can be reasonably argued, based upon the published intestinal perfusion studies,

4, 5

that the gut permeability value (Peff) for FEXO may be approximately 1.7-fold


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higher than used by Sjogren et al (0.12 vs. 0.07). 1 For a poorly permeable drug such as FEXO, such a difference will have a marked impact upon the simulated fraction absorbed. FEXO is also suspected to have significant biliary clearance in humans

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which gives rise to the possibility that the drug

undergoes repeated cycles of EHR thus increasing fraction absorbed and thence exposure. Whilst EHR can be handled by the commercial programs this mechanism is not accounted for in any part of the procedure used by Sjogren et al.

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- not least because biliary clearance in humans has not been

quantified to the knowledge of the authors of this Letter. It is simply not possible to make a definitive assessment of the relative predictive performance of the software platforms in such a situation. It should be also noted that with Simcyp the six FEXO formulations make up six of the eleven mostly poorly predicted studies including the most poorly predicted study of those in the Sjogren et al.

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paper. 2. What is a software platform default value for a drug? Unless Sjogren et al.

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intended to demonstrate the ramifications of naïve use of software, applying

default values for critical parameters is inevitably associated with mal-prediction. For parameter(s) lacking an experimental value, or with large experimental uncertainty, a standard approach is to perform sensitivity analysis – the modeller can then decide if there is a need for (additional) experimental measurements or, as a minimum, report the alternative possible simulation outcomes based upon a reasonable range of parameter values. This issue is best understood with a specific example. Sjogren et al.

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have, quite correctly, not

assumed default values (that is to say, the values that first appear on-screen when setting up a simulation) for compound type (acid, base, ampholyte) and pKa, solubility (intrinsic or biorelevant), gut wall permeability amongst others. However, for supersaturation and precipitation related parameters they accepted default values without any apparent attempt to use in silico or in cerebro prior knowledge or indeed conduct sensitivity analysis. It is well known that the properties of supersaturated solutions are highly variable depending (at least) upon the drug itself, the environmental conditions and even the history of the situation viz. the rate at which supersaturated



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state was achieved. With this in mind there cannot, to highlight one key aspect, be a reasonable default parameter to describe the duration of the metastable state. There are several clinical studies indicating how greatly the precipitation kinetics can vary among drugs of similar BCS class (and among different formulations of the same drug).

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Nonetheless all three simulation tools require a

default set of actions to take when a supersaturated concentration is obtained and thus signal to the modeller that supersaturated conditions have been identified as an important parameter. It is therefore common sense (good practice) for a modeller to examine the outputs from a simulation and take appropriate action regardless of whether supersaturated conditions are expected by the modeller. In this instance the bounds of the sensitivity analysis, in the absence of any other information, would be an enduring metastable state (i.e., no precipitation at all in the simulation time-frame) on the one hand, and immediate, rapid precipitation on the other hand. For example, for FEXO 240 mg the Simcyp simulated fa ranges from ~4% (default precipitation parameters and as reported by Sjogren et al. 1) to 25% (no precipitation). This is expected to make a significant difference to the simulated exposure, even assuming the absence of any other mitigating mechanisms impacting on the exposure of FEXO such as enterohepatic recirculation, 6 which is not accounted for in any of the simulations of Sjogren et al. 1 Interestingly, Sjogren et al.

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adjusted one of the precipitation kinetics parameters in the GI-Sim

model specifically for ketoconazole but kept the default values in both other platforms. The authors did not mention how critical that parameter was in the GI-Sim model and what would have been predicted if that parameter remained at the default value in that model. The most obvious approach, in the absence of direct knowledge of in vivo behaviour, as is usually the case, is to obtain prior estimates of the relevant parameters from the modelling of appropriate in vitro experiments. This is common practice in the case of the estimation of gut wall permeability from in vitro cell line experiments and indeed was applied by Sjogren et al. 1 where required for the three platforms, but for supersaturation and precipitation modelling the default model and/or with the default parameters provided by Simcyp and GastroPlusTM was chosen without further explanation.


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3. What is the reference point for assessment of the simulation results? The primary selection criterion for the API used in this study was that they exhibit incomplete absorption (fa < 1) and as a consequence they are poorly soluble and/or poorly permeable. The PK of such compounds is expected to be increasingly variable between individuals as the bioavailability decreases.

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Therefore, the selection of the reference clinical studies against which to assess

simulation performance may have a crucial impact on the decision regarding success or failure of a prediction. We have previously commented on this aspect of simulation and provided literature values of kinetic parameters and their variability.

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If there are several published clinical studies, the best

practice is to: critically analyse the available data and exclude studies where, for example, protocol issues are identified; run meta-analysis of the studies retained followed by heterogeneity tests to assess for the presence of outliers; and finally to perform a formal meta-analysis and use the weighted mean of the studies considering the study size. For many of the API/formulation/dose combinations in the Sjogren et al. 1 study it appears that there are very few clinical studies publically available so this type of exercise is difficult to perform. However, we have searched the literature for a subset of the compounds and found that for fenofibrate (145 and 160 mg) the clinical study chosen by Sjogren et al.1 as the reference reported mean AUCs that are significantly lower than the weighted mean of all the studies collected (120,000 vs 178,000 ng/mL.h for the 145 mg dose and 100,000 vs 130,000 ng/mL.h for the 160 mg dose). With this choice of reference data, all three platforms appear to be under-predicting the AUC for fenofibrate and the simulations suggest that the input data are incorrect and/or mechanisms are missing from the models. Again it should be noted that the two fenofibrate Simcyp simulations make up two of the twelve most poorly predicted studies. While this is not a comprehensive analysis these findings lead one to question the strong conclusion drawn by the authors regarding the comparative performance of software platforms.



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4. Should PBPK models use non-physiologic system parameter values and models? This final issue might be considered somewhat philosophical, although we consider it to be critical to the continuing development of mechanistic absorption models. The question is whether system (i.e., physiological and anatomical) parameters should reflect current knowledge of physiology, an arbitrary value, or be adjusted to optimise simulation outcomes (be this on a case-by-case basis or across a range of studies). The majority of the studied drugs are poorly soluble and all are incompletely absorbed and so the mass (concentration) of drug dissolved in the gut lumen fluids is for these compounds one of the most critical factors in the simulation of the overall rate and extent of absorption (leaving aside other factors including transporter and first pass metabolism effects, enterohepatic recirculation and so on). A priori the selection of appropriate luminal fluid volumes is expected to have a major influence on simulation outcomes. Both GastroPlusTM version 8.0.0002 (used by Sjogren et al. 1) and GI-Sim use artificially high luminal fluid volumes - Sjogren et al.

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cite Heikkinen et al. (2012)

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for water

volumes which in fact references GastroPlusTM version 7.0.0028). Thus the fluid volumes used for GISim and GastroPlusTM platforms are similar and are, for the majority of the small intestine regions, manifold higher than the available physiological measurements established by Magnetic Resonance Imaging (MRI) or Positron Emission Tomography (PET) imaging techniques

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and indeed as

previously estimated via gastrointestinal modelling with GastroPlusTM by Sutton (2009) 16. This raises a question, at least in our mind, that the un-physiologically high water volumes may be compensating for any one of numerous other mechanisms which may or may not be correctly accounted for by, or included in, the models, including inaccurate translation of Peff to the gut wall permeation rate, contribution of lymphatic transport, trapping of particles of lipophilic drugs in the gut wall mucus, EHR or lack of understanding of the bioavailability of drug from the different regions of the large intestine in particular including residence times (Arhan et al. 1981) etc. 17 Moreover, the lack of understanding of drug behaviour in the colon is stated by Sjogren et al.

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as

grounds for the absence of a colon compartment in the GI-Sim platform. While we completely agree


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that the mechanisms controlling the bioavailability of drug from the colon are poorly understood, it is inappropriate to come to a strong, definitive conclusion regarding the accuracy of one software platform compared to another without knowledge from appropriate clinical studies of the contribution of colonic absorption to the overall fraction absorbed. Indeed, a very useful evaluation 18 suggests that colonic drug absorption may be significant at least for some drugs. However, the drugs studied were for the most part dosed directly into the colon in solution which means the fluid available may be quite different to that available to the drug arriving in the colon after oral dosing making it difficult to exploit the data from this study. Final Comments The rate and extent of absorption of drugs from the gastrointestinal tract and ultimately their systemic bioavailability is governed by complex mechanisms, not all of which are understood or well characterised particularly so for the colon. For this reason alone, and as partly described in the preceding text, it is very difficult to arrive at a definitive conclusion regarding the relative performance of the three chosen mechanistic software platforms based on the analyses presented by Sjogren et al. 1 Indeed, it is our assertion that from the Sjogren et al. 1 study it can only be stated that with the given set of APIs, input parameters and model options (default or otherwise) Simcyp provides the more conservative predictions of exposure of the three platforms. However, given the lack of understanding of the rate and extent of absorption from the colon even this statement may be misleading. Sjogren et al. 1 have missed the opportunity to explore the scientific issues we now raise in this letter and have instead focussed upon a relatively small set of incompletely characterised APIs while avoiding any real discussion of the uncertainties involved in modelling these compounds including those outlines above. The GI-Sim simulations cannot be duplicated easily as it is not a publically available platform, but we encourage investigators with access to GastroPlusTM and Simcyp to re-run the simulations and examine the impact of the assumptions made by Sjogren et al.

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The larger study by the OrBiTo



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consortium (to be submitted soon and initially presented in the OrBiTo Open Meeting in June 2015) demonstrates the high impact of the decisions made by the modeller with regard to input parameters and model selection, as opposed to differences between the platforms themselves. We hope that by raising and discussing the above issues all investigators who use and/or compare PBPK tools will have cause to deliberate on their selection of input parameters, model choices and selection of reference studies. Conflict of Interest DBT, BL, NP, SMP, SP, MJ and ARH are all employees of Simcyp Limited (A Certara Company). Simcyp and Goethe University are members of the OrBiTo Consortium funded under the European Innovative Medicine Initiative (IMI). The authors DBT, BL, NP, SMP, MJ, JD and ARH are active participants in OrBiTo.


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